SKILLS/analyzing-packed-malware-with-upx-unpacker/SKILL.md
Identifies and unpacks UPX-packed and other packed malware samples to expose the original executable code for static analysis. Covers both standard UPX unpacking and handling modified UPX headers that prevent automated decompression. Activates for requests involving malware unpacking, UPX decompression, packer removal, or preparing packed samples for analysis.
npx skillsauth add pinkpixel-dev/skills-collection-1 analyzing-packed-malware-with-upx-unpackerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Do not use when dealing with custom packers, VM-based protectors (Themida, VMProtect), or samples where dynamic unpacking via debugging is more appropriate.
apt install upx-ucl or download from https://upx.github.io/)pefile library for manual header repairDetermine if the sample is packed and identify the packer:
# Check with Detect It Easy
diec suspect.exe
# Check with UPX (test without unpacking)
upx -t suspect.exe
# Python-based entropy and packer detection
python3 << 'PYEOF'
import pefile
import math
pe = pefile.PE("suspect.exe")
print("Section Analysis:")
for section in pe.sections:
name = section.Name.decode().rstrip('\x00')
entropy = section.get_entropy()
raw = section.SizeOfRawData
virtual = section.Misc_VirtualSize
print(f" {name:8s} Entropy: {entropy:.2f} Raw: {raw:>8} Virtual: {virtual:>8}")
# Check for UPX section names
section_names = [s.Name.decode().rstrip('\x00') for s in pe.sections]
if 'UPX0' in section_names or 'UPX1' in section_names:
print("\n[!] UPX section names detected")
elif '.upx' in [s.lower() for s in section_names]:
print("\n[!] UPX variant section names detected")
# Check import count (packed binaries have very few)
if hasattr(pe, 'DIRECTORY_ENTRY_IMPORT'):
total_imports = sum(len(e.imports) for e in pe.DIRECTORY_ENTRY_IMPORT)
print(f"\nTotal imports: {total_imports}")
if total_imports < 10:
print("[!] Very few imports - likely packed")
else:
print("\n[!] No import directory - heavily packed")
PYEOF
Try the built-in UPX decompression:
# Standard UPX decompress
upx -d suspect.exe -o unpacked.exe
# If UPX fails with "not packed by UPX" error, the headers may be modified
# Verbose output for debugging
upx -d suspect.exe -o unpacked.exe -v 2>&1
# Verify the unpacked file
file unpacked.exe
diec unpacked.exe
If standard decompression fails, repair tampered magic bytes:
# Repair modified UPX headers
import struct
with open("suspect.exe", "rb") as f:
data = bytearray(f.read())
# UPX magic bytes: "UPX!" (0x55505821)
# Malware authors commonly modify these to prevent automatic unpacking
# Search for modified UPX signatures
upx_magic = b"UPX!"
modified_patterns = [b"UPX0", b"UPX\x00", b"\x00PX!", b"UPx!"]
# Find and restore section names
pe_offset = struct.unpack_from("<I", data, 0x3C)[0]
num_sections = struct.unpack_from("<H", data, pe_offset + 6)[0]
section_table_offset = pe_offset + 0x18 + struct.unpack_from("<H", data, pe_offset + 0x14)[0]
print(f"PE offset: 0x{pe_offset:X}")
print(f"Number of sections: {num_sections}")
print(f"Section table offset: 0x{section_table_offset:X}")
for i in range(num_sections):
offset = section_table_offset + (i * 40)
name = data[offset:offset+8]
print(f"Section {i}: {name}")
# Restore UPX magic bytes in the binary
# Search for the UPX header signature location (typically near the end of packed data)
for i in range(len(data) - 4):
if data[i:i+3] == b"UPX" and data[i+3] != ord("!"):
print(f"Found modified UPX magic at offset 0x{i:X}: {data[i:i+4]}")
data[i:i+4] = b"UPX!"
print(f"Restored to: UPX!")
# Also restore section names if modified
for i in range(num_sections):
offset = section_table_offset + (i * 40)
name = data[offset:offset+8].rstrip(b'\x00')
if name in [b"UPX0", b"UPX1", b"UPX2"]:
continue # Already correct
# Check for common modifications
if name.startswith(b"UP") or name.startswith(b"ux"):
original = f"UPX{i}".encode().ljust(8, b'\x00')
data[offset:offset+8] = original
print(f"Restored section name at 0x{offset:X} to {original}")
with open("suspect_fixed.exe", "wb") as f:
f.write(data)
print("\nFixed file written. Retry: upx -d suspect_fixed.exe -o unpacked.exe")
When automated unpacking fails entirely, use dynamic unpacking:
Manual UPX Unpacking with x64dbg:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Load packed sample in x64dbg
2. Run to the entry point (system breakpoint then F9)
3. UPX unpacking stub pattern:
a. PUSHAD (saves all registers)
b. Decompression loop (processes packed sections)
c. Resolves imports (LoadLibrary/GetProcAddress calls)
d. POPAD (restores registers)
e. JMP to OEP (original entry point)
4. Set hardware breakpoint on ESP after PUSHAD:
- After PUSHAD, right-click ESP in registers -> Follow in Dump
- Set hardware breakpoint on access at [ESP] address
- Run (F9) - breaks at POPAD before JMP to OEP
5. Step forward (F7/F8) until you reach the JMP to OEP
6. At OEP: Use Scylla plugin to dump and fix imports:
- Plugins -> Scylla -> OEP = current EIP
- Click "IAT Autosearch" -> "Get Imports"
- Click "Dump" to save unpacked binary
- Click "Fix Dump" to repair import table
Verify the unpacked sample is valid and complete:
# Verify unpacked PE is valid
python3 << 'PYEOF'
import pefile
pe = pefile.PE("unpacked.exe")
# Check sections are normal
print("Unpacked Section Analysis:")
for section in pe.sections:
name = section.Name.decode().rstrip('\x00')
entropy = section.get_entropy()
print(f" {name:8s} Entropy: {entropy:.2f}")
# Verify imports are resolved
print(f"\nImport count:")
if hasattr(pe, 'DIRECTORY_ENTRY_IMPORT'):
for entry in pe.DIRECTORY_ENTRY_IMPORT:
dll = entry.dll.decode()
count = len(entry.imports)
print(f" {dll}: {count} functions")
total = sum(len(e.imports) for e in pe.DIRECTORY_ENTRY_IMPORT)
print(f" Total: {total} imports")
# Compare file sizes
import os
packed_size = os.path.getsize("suspect.exe")
unpacked_size = os.path.getsize("unpacked.exe")
print(f"\nPacked: {packed_size:>10} bytes")
print(f"Unpacked: {unpacked_size:>10} bytes")
print(f"Ratio: {unpacked_size/packed_size:.1f}x")
PYEOF
| Term | Definition | |------|------------| | Packing | Compressing or encrypting executable code to reduce file size and hinder static analysis; the binary contains an unpacking stub that restores code at runtime | | UPX | Ultimate Packer for eXecutables; open-source executable packer commonly abused by malware authors because it is free and effective | | Original Entry Point (OEP) | The real starting address of the malware code before packing; the unpacking stub decompresses code then jumps to the OEP | | Import Reconstruction | Process of rebuilding the import address table after dumping an unpacked process from memory using tools like Scylla or ImpRec | | PUSHAD/POPAD | x86 instructions that save/restore all general-purpose registers; UPX uses this pattern to preserve register state during unpacking | | Section Entropy | Randomness measure of PE section data; packed sections show entropy > 7.0 while normal code sections average 5.0-6.5 | | Magic Bytes | Signature bytes within a file identifying its format; UPX uses "UPX!" which malware authors modify to prevent automated decompression |
Context: A malware sample is identified as UPX-packed by section names (UPX0, UPX1) but upx -d fails with "CantUnpackException: header corrupted". The malware author modified the UPX magic bytes to prevent automated decompression.
Approach:
upx -d on the repaired binaryPitfalls:
UNPACKING ANALYSIS REPORT
===========================
Sample: suspect.exe
SHA-256: e3b0c44298fc1c149afbf4c8996fb924...
Packer: UPX 3.96 (modified headers)
PACKED BINARY
Sections: UPX0 (entropy: 0.00) UPX1 (entropy: 7.89) .rsrc (entropy: 3.45)
Imports: 2 (kernel32.dll: LoadLibraryA, GetProcAddress)
File Size: 98,304 bytes
UNPACKING METHOD
Method: Header repair + UPX -d
Header Fix: Restored UPX! magic at offset 0x1F000
Command: upx -d suspect_fixed.exe -o unpacked.exe
Result: SUCCESS
UNPACKED BINARY
Sections: .text (entropy: 6.21) .rdata (entropy: 4.56) .data (entropy: 3.12) .rsrc (entropy: 3.45)
Imports: 147 (kernel32, user32, advapi32, wininet, ws2_32)
File Size: 245,760 bytes (2.5x expansion)
OEP: 0x00401000
VALIDATION
PE Valid: Yes
Imports Resolved: Yes (147 functions across 8 DLLs)
Executable: Yes (runs without crash in sandbox)
NEXT STEPS
- Import unpacked.exe into Ghidra for full disassembly
- Run YARA rules against unpacked binary
- Submit unpacked binary to VirusTotal for improved detection
testing
When the user wants a full ASO health audit, review their App Store listing quality, or diagnose why their app isn't ranking. Also use when the user mentions "ASO audit", "ASO score", "why am I not ranking", "listing review", or "optimize my app store page". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
testing
Clarify requirements before implementing. Use when serious doubts arise.
tools
Complete reference and build guide for ASI:One (ASI1) — the AI platform by Fetch.ai built for agentic, Web3-native applications. Use this skill IMMEDIATELY and ALWAYS when the user mentions ASI1, ASI:One, Fetch.ai AI API, building with ASI1, integrating ASI:One, asking about ASI1 models, tool calling with ASI1, ASI1 image generation, ASI1 agentic LLM, Agentverse, uagents, Agent Chat Protocol, structured output with ASI1, or OpenAI-compatible wrappers for ASI1. Also trigger when the user says things like "use ASI1 instead of OpenAI", "build an app with ASI:One", "ASI1 API", or references docs.asi1.ai. This skill covers everything needed to build production apps - setup, all models, all API features, tool calling, image gen, agentic orchestration, structured data, session management, streaming, LangChain integration, uagents / Agent Chat Protocol, and TypeScript/Node.js patterns.
data-ai
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.